Optimized video review using motion recap images

ABSTRACT

Systems and methods provide for optimizing video review using motion recap images. A video review system can identify background image data of a video clip including an amount of motion satisfying a motion threshold. The video review system can generate foreground mask data segmenting foreground image data, representing a moving object in the video clip, from the background image data. The video review system can select a set of instances of the moving object represented in the foreground image data. The video review system can generate a motion recap image by superimposing the set of instances of the moving object represented in the foreground image data onto the background data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/406,516, filed May 8, 2019, which claims thebenefit of U.S. Provisional Patent Application No. 62/826,960, filedMar. 29, 2019, the full disclosures of which are incorporated herein byreference in their entireties.

TECHNICAL FIELD

The subject matter of this disclosure relates in general to the field ofcomputer vision, and more specifically for systems and methods foroptimizing video review using motion recap images.

BACKGROUND

Security cameras are often necessary to keep locations, such as homes,businesses, and other places that may be important to users, safe,secure, and operating smoothly. However, the time and expense to reviewvideo footage from the security cameras can be prohibitive for bothconsumers and enterprises alike. For example, inspecting video toidentify “interesting” events can involve streaming and analyzing hoursor even days of footage, and can thus be a painstaking andresource-intensive process for users. Some security camera systems maybe capable of excerpting clips meeting certain criteria of interest. Butreviewing these clips can still come at a high cost in terms of time,money, and other resources (e.g., Central Processing Unit (CPU), memory,storage, bandwidth, power, etc.) when there are a large number of clipsand/or individual clips are lengthy.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments that are illustrated inthe appended drawings. Understanding that these drawings depict onlyembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIGS. 1A-1B illustrate block diagrams of an example of a networkenvironment and a video review system in accordance with an embodiment;

FIGS. 2A-2C illustrate an example of an approach for processing video toidentify video clips of interest in accordance with an embodiment;

FIG. 3 illustrates a flow chart of an example of a process foridentifying video clips of interest in accordance with an embodiment;

FIGS. 4A-4E illustrate examples of graphical user interfaces for a videoreview system in accordance with some embodiments;

FIG. 5 illustrates a flow chart of an example of a process forgenerating a motion recap image in accordance with an embodiment; and

FIGS. 6A-6B illustrate block diagrams of examples of computing systemsin accordance with some embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The detailed description set forth below is intended as a description ofvarious configurations of embodiments and is not intended to representthe only configurations in which the subject matter of this disclosurecan be practiced. The appended drawings are incorporated herein andconstitute a part of the detailed description. The detailed descriptionincludes specific details for the purpose of providing a more thoroughunderstanding of the subject matter of this disclosure. However, it willbe clear and apparent that the subject matter of this disclosure is notlimited to the specific details set forth herein and may be practicedwithout these details. In some instances, structures and components areshown in block diagram form in order to avoid obscuring the concepts ofthe subject matter of this disclosure.

Overview

Systems and methods provide for optimizing video review using motionrecap images. A video review system can identify background image dataof a video clip including an amount of motion satisfying a motionthreshold. The video review system can generate foreground mask datasegmenting foreground image data, representing a moving object in thevideo clip, from the background image data. The video review system canselect a set of instances of the moving object represented in theforeground image data. The video review system can generate a motionrecap image by superimposing the set of instances of the moving objectrepresented in the foreground image data onto the background data.

DESCRIPTION

Current video review systems exist that can provide full digitalsolutions for designing surveillance systems, beginning with the sensorsthat capture raw data (e.g., image, audio, video (e.g., image andaudio), Infrared (IR), thermal, motion, etc.) to the presentation ofrelevant information for review by users. Some of these video reviewsystems may also be capable of automatically detecting events ofinterest, such as video clips that include (or exclude) motion, personsor other objects of interest, anomalies, and so forth. However, thevolume of video generated by these conventional systems that must bereviewed can be burdensome for users and taxing on resources (e.g., CPU,memory, storage, bandwidth, power, etc.).

Various embodiments of the present disclosure may overcome the above andother deficiencies of the prior art by generating motion recap imagesfor more efficient review of video data. The motion recap images cancomprise still images that summarize the motion in a video clip bysuperimposing or stacking instances of a moving object from multipleimage frames onto a single frame. This can enable a user to glance atone or more motion recap images to determine whether to furtherinvestigate their corresponding video clips. The user can quickly skipover video clips of little to no interest, and the video review systemcan operate more efficiently by decreasing utilization of CPU, memory,storage, bandwidth, and so forth, that would otherwise be consumed forprocessing the video clips. In some embodiments, generation of themotion recap images can occur locally. This can ensure efficient videoreview is available even in the event of network failure, provide forincreased privacy and security, and further improve resource utilizationby eliminating the need for server and Graphic Processing Unit (GPU)farms in the cloud. Instead of a conventional thumbnail image consistingof a single frame that may not immediately convey the relevance of avideo clip and requiring video playback, the present technology cangenerate a motion recap image that summarizes the contents of the videoclip. Numerous other functions and advantages are described andsuggested below as may be provided in accordance with the variousembodiments.

FIG. 1A illustrates a block diagram of an example of a networkenvironment 100 in which a video review system can be deployed. One ofordinary skill in the art will understand that, for the networkenvironment 100 and any other system discussed in the presentdisclosure, there can be additional or fewer component in similar oralternative configurations. The illustrations and examples provided inthe present disclosure are for conciseness and clarity. Otherembodiments may include different numbers and/or types of elements butone of ordinary skill the art will appreciate that such variations donot depart from the scope of the present disclosure.

In this example, the network environment 100 can include one or morecameras 102 (e.g., video cameras, box cameras, dome cameras, Point TiltZoom (PTZ) cameras, bullet cameras, C-mount cameras, Internet Protocol(IP) cameras, Long-Term Evolution (LTE™) cameras, day/night cameras,thermal cameras, wide dynamic cameras, Closed-Circuit Television (CCTV)cameras, Network Video Recorders (NVRs), wireless cameras, smartcameras, indoor cameras, outdoor cameras, etc.) configured for recordingdata of a scene 104, including image data as a series of frames 106,audio data (not shown), and other sensor data (e.g., IR, thermal,motion, etc.) (not shown). In some embodiments, Cisco Meraki® MV seriescameras may be deployed as the cameras 102.

The network environment 100 also includes one or more computing devices108 for providing local access to the data captured by the cameras 102.The computing devices 108 can be general purpose computing devices(e.g., servers, workstations, desktops, laptops, tablets, smart phones,etc.), wearable devices (smart watches, smart glasses or other smarthead-mounted devices, smart ear pods or other smart in-ear, on-ear, orover-ear devices, etc.), televisions, digital displays, and any otherelectronic devices that are capable of connecting to or are integratedwith the cameras 102 and incorporating input/output components to enablea user to locally access the cameras' data.

The network environment can also include a Wide-Area Network (WAN) 110.In general, the WAN 110 can connect geographically dispersed devicesover long-distance communications links, such as common carriertelephone lines, optical light paths, synchronous optical networks(SONETs), synchronous digital hierarchy (SDH) links, Dense WavelengthDivision Multiplexing (DWDM) links, and so forth. The WAN 110 may be aprivate network, such as a T1/E1, T3/E3, or other dedicated or leasedline network; a Public Switched Telephone Network (PSTN), IntegratedServices Digital Network (ISDN), or other circuit-switched network;Multi-Protocol Label Switching (MPLS), Ethernet WAN (also sometimesreferred to as Metropolitan Ethernet or MetroE, Ethernet over MPLS orEoMPLS, Virtual Private Local Area Network (LAN) Service (VPLS), etc.),Frame Relay, Asynchronous Transfer Mode (ATM), or other packet-switchednetwork; Very Small Aperture Terminal (VSAT) or other satellite network;and so forth.

The WAN 110 can also be a public network, such as the Internet. TheInternet can connect disparate networks throughout the world, providingglobal communication between devices on various networks. The devicesmay communicate over the network by exchanging discrete frames orpackets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). Access to theInternet can be provided over a Digital Subscriber Line (DSL), cable,fiber, or wirelessly, such as via Municipal Wi-Fi, WorldwideInteroperability for Microwave Access (WiMAX), satellite Internet, or acellular network (e.g., 3G, 4G, 5G, etc.), among other possibilities.

In some embodiments, the cameras 102 may use the WAN 110 to connect to acloud-based network management system to provide additional services,such as centralized management of network devices (e.g., routers,switches, access points, security appliances, gateways, etc.),Software-Defined WANs (SD-WANs), Wireless Local Area Network (WLANs),endpoints (e.g., computing devices, IP phones, etc.), and so forth. Insome embodiments, the Cisco Meraki® platform may be used for thecloud-based management system. The Cisco Meraki® platform may beespecially advantageous because it can also provide various advancedcamera features, such as zero-touch configuration, cloud-augmented edgestorage, security patching and software updates, video archival, videosearch indexing, and remote access (e.g., access by one or morecomputing devices 112 not locally connected to the cameras 102), amongother functionalities.

FIG. 1B illustrates a block diagram of an example of a video reviewsystem 120. In some embodiments, the video review system 120 may bephysically integrated with the cameras 102. In other embodiments, someor all of the functionality of one or more modules of the video reviewsystem 120 may be additionally or alternatively implemented by thecomputing devices 108 or 112, one or more computing devices of acloud-based management system accessible via the WAN 110, and the like.In this example, the video review system 120 includes an imageprocessing module 122, a data retention module 124, a search module 126,a motion recap image module 128, a management dashboard 130, an accesscontrol module 132, an analytics module 134, a security module 136, anda data store 138.

The image processing module 122 can process image data of the scene 104captured by image sensors (not shown) of the cameras 102 to generate andstore the frames 106, such as in the data store 138 and/or a remote datastore accessible via the WAN 110. In addition, the image processingmodule 122 can analyze the frames 106 to detect motion occurring withinthe scene 104. As discussed in further detail below, motion can bedetected by calculating a sum of absolute differences between the frames106 on a frame-by-frame basis. Motion can then be indexed based onvarious indicators, such as a timestamp, duration, and/or an intensityassociated with the frame. Subsequently, data describing the motion(“motion metadata”) may be stored, such as in the data store 138 and/ora remote data store accessible via the WAN 110.

In some embodiments, the image processing module 122 can analyze theframes 106 locally to detect motion and index the video with the motionmetadata and other metadata remotely. This hybrid-based approach canprovide users maximum flexibility over how to store camera data. Forexample, the data retention module 124 can enable users to select avideo bit rate and frame rate to find the optimal balance betweenstorage length and image quality. In some embodiments, the dataretention module 124 can also support cloud-augmented edge storage bystoring a configurable amount of footage and motion metadata locally(e.g., the last 72 hours) before intelligently trimming stored videothat may not include any motion. The data retention module 124 can alsosupport scheduled recording to control if and when the cameras 102 arerecording. In addition, the data retention module 124 can allow users tocreate schedule templates that may be applied to groups of cameras andto store only the data that may be needed. Recording can also be turnedoff altogether and live footage may be reviewed for selective privacy.The data retention module 124 can also provide real-time retentionestimates for how long certain video data may be stored locally and/orremotely.

The search module 126 can be used to facilitate queries relating tovideo data, including motion search queries requesting for any of theframes 106 or sets of the frames (e.g., sequences or clips) that mayinclude motion. For example, the search module 126 can enable a user toprovide a motion search query (e.g., as input to the cameras 102, thecomputing devices 108 or 112, etc.). The search query can be used tosearch the data store 138 or transmitted for processing by a remoteserver accessible via the WAN 110. As discussed in further detail below,the motion search query can include boundary conditions indicating aparticular area or region within the frames in which motion events areto be searched. By permitting users to specify a particular “region ofinterest” for their motion search query, video data can be efficientlysearched only for events occurring at indicated locations of interest,increasing the speed and efficiency of the search, as well as reducingoverhead due to processing and other resources.

The motion recap image module 128 can generate one or more motion recapimages from the search results or video clips responsive to the motionsearch query.

The management dashboard 130 can provide a user interface forconfiguring the cameras 102 and accessing camera data. In someembodiments, the management dashboard 130 may be integrated with theuser interface of a cloud-based management system, such as the CiscoMeraki® dashboard. Integration with the Cisco Meraki® dashboard may beespecially advantageous because of some of the features the dashboardcan provide, such as zero-touch configuration (e.g., using just serialnumbers, an administrator can add devices to the dashboard and beginconfiguration even before the hardware arrives on site, users can streamvideo and configure cameras across multiple locations without having toconfigure an IP or installing a plugin, etc.), remote troubleshooting,centralized management, and the video wall.

The access control module 132 can provide differentiated access to userswith granular controls appropriate for their particular roles. Forexample, the access control module 132 can give a receptionist access toa video camera located at the front door but may not give him/her fullcamera configuration privileges, prevent security staff from changingnetwork settings, limit views to selected cameras, restrict the exportof video, and so forth. In some embodiments, the access control module132 can also support Security Assertion Markup Language (SAML).

The analytics module 134 can provide the video review system 120 withvarious data science, computer vision, and machine learningcapabilities, such as real-time and/or historical data analytics, heatmaps, and person detection features. For example, the analytics module134 can provide Message Queuing Telemetry Transport (MQTT) and RestfulState Transfer (REST) Application Programming Interfaces (API) endpointsto enable organizations to integrate the edge-computing capabilities ofthe cameras 102 into the organizations' software systems, and providethe organizations with high-value (real-time and/or historical data) andinsights without additional infrastructure. REST is a design pattern inwhich a server enables a client to access and interact with resourcesvia Uniform Resource Identifiers (URIs) using a set of predefinedstateless operations (referred to as endpoints).

MQTT is a client-server publish/subscribe messaging transport protocolthat is lightweight, open, simple, and designed to be easy to implement.These characteristics can make it ideal for use in constrainedenvironments, such as for communication in Machine-to-Machine (M2M) andInternet of Things (IoT) contexts where a small code footprint may berequired or network bandwidth is at a premium. The MQTT protocol can runover TCP/IP, or over other network protocols that provide ordered,lossless, bi-directional connections.

The analytics module 134 can also generate heat maps. For example, theanalytics module 134 can use motion metadata to generate the heat mapsto show an overview of historical motion, such the last week's worth ofmotion data, on a per-day or per-hour basis, to help users understandhow a space is being used. The analytics module 134 can present a seriesof heat maps for each unit of time per selected resolution (e.g., 1hour, 1 day, etc.). Areas of motion observed across the heat maps inthis series can be given an absolute value relative to the total amountof motion. The analytics module 134 can display a range of colors toindicate motion in the area, such as from red to indicate a large amountof motion, green to indicate small amount of motion, and orange toyellow to indicate amounts of motion in between. Areas where very littleor no motion may not be represented, as they may be insignificantcompared to motion observed in other areas.

Person detection features can include the ability to detect persons invideo data from a camera feed. For example, objects detected as personsmay be enclosed by yellow boxes. The analytics module 134 can alsogenerate histograms of people detected by the cameras 102 (e.g., percamera, per set of cameras, or for all cameras) and record statisticsabout how many persons entered or were present in a location at aspecified time (e.g., per minute, hour, day, etc.), the hour or othertime period that the location was most utilized, peak occupancy, totalentrances, and so forth. The analytics module 134 can also identifyanomalies when usage differs from historical trends.

The security module 136 can provide features such as Public KeyInfrastructure (PKI) encryption for each camera 102 of the video reviewsystem 120 and two-factor authentication for access to the video reviewsystem 120. The security module 136 can also ensure that local video isencrypted by default to provide an additional layer of security thatcannot be deactivated. In addition, the security module 136 canautomatically manage software updates and security patches according toscheduled maintenance windows.

The data store 138 can be used for saving video, motion metadata, and soforth. In some embodiments, the data store 138 may be implemented usingSolid State Devices (SSDs) on each camera 102. This can ensure thatcamera data continues to be recorded even when there is no networkconnectivity. In some embodiments, the data store 138 may also beconfigured using a distributed architecture such that the storage of thevideo review system 120 can scale with the addition of each camera 102and to support high availability/failover protection.

FIGS. 2A-2C illustrate an example of an approach for processing videodata to identify video clips of interest. In some embodiments, a videoreview system, such as the video review system 120, may identify videoclips of interest as sequences of frames containing a specified amountof motion. In other embodiments, the video review system may identifyvideo clips of interest as sequences containing (or not containing)representations of faces, persons, specific faces or persons,unrecognized faces or persons, or other objects; sequences containingrepresentations of certain actions or activities (e.g., fans brawling inthe stands, vehicles running red lights, malfunctioning equipment,etc.), anomalous sequences, and so forth.

FIG. 2A illustrates an example of a frame 200 that the video reviewsystem 120 can capture and divide into cells 202 (e.g., A1, A2, . . .I10). In this example, the cells 202 are arranged in a 9×10 gridsuperimposed over the frame 200. However, one of ordinary skill willunderstood that other numbers of cells, cell or frame shapes, cell orframe sizes, and so forth, can be used, without departing from the scopeof the present technology. As discussed in further detail below, motiondetection can be performed using subdivided units of the cells 202 thatcan represent the smallest collection of adjacent pixels for whichmotion is detected (referred to herein as a sub-cell or a block of (oneor more) pixels). As discussed in greater detail below, the motionassociated with a particular sub-cell can be identified by calculatingabsolute differences (e.g., in pixel color or intensity) as betweencommon sub-cells in adjacent frames (e.g., on a frame-to-frame basis).

FIG. 2B illustrates an example of a portion 220 of the frame 200 (e.g.,cells A1, A2, B1, and B2) in which the cell 202 (A1) may be furtherdivided into sub-cells 222 (e.g., A1-0, A1-1, . . . , A1-23). Thesub-cells 222 can be used to distinguish regions within the cell 202(A1) in which motion is detected. In this example, the cell 202 (A1) cancomprise twenty-four sub-cells 222. However, one of ordinary skill inthe art will appreciate that any other number of sub-cells, sub-cellshapes, sub-cell sizes, and so forth, may be used to subdivide each cell202, without departing from the scope of the present technology.

In some embodiments, calculations of motion can be determined based onchanges in pixel values for a particular sub-cell across multipleframes. In this example, the sub-cells 222 may cover an area of theframe 200 that does not contain any motion. In contrast, FIG. 2Cillustrates an example of a portion 240 of the frame 200 (e.g., cell 202(D7)) in which motion is detected. In particular, FIG. 2C showsun-shaded sub-cells that include an amount of motion satisfying athreshold (e.g., the sub-cells 7, 10, 11, 13-15, 18, and 19) and shadedsub-cells that do not include an amount of motion satisfying thethreshold (e.g., sub-cells 0-6, 8, 9, 12, 16, 17, and 20-23).

FIG. 2C also shows a motion vector 242 corresponding to the cell 202(D7). In some embodiments, motion vectors, such as the motion vector242, can be used to provide a compressed data format for storing motiondata. For example, the first four bits of the motion vector 242 canrepresent the row of the cell 202 (D7) (e.g., “D” or “0011”), the nextfour bits can represent the column (e.g., “7” or “0110”), and theremaining bits can represent a bit vector corresponding to the sub-cells222 and indicating that a given sub-cell includes an amount of motionsatisfying the threshold by a value of ‘1’ (e.g., 7, 10, 11, 13-15, 18,and 19) and that a given sub-cell does not include an amount of motionsatisfying the threshold by a ‘0’ (e.g., 0-6, 8, 9, 12, 16, 17, and20-23). Here, motion within the cell 222D7 may be identified atsub-cells 13, 10, 14, 18, 7, 11, 15, and 19. In some embodiments, motionvectors, such as the motion vector 242, can be associated with arespective frame that is sorted as a times-series database (that may bestored in the data store 138 and/or a remote data store accessible viathe WAN 110).

Utilization of motion vectors, such as the motion vector 242, can beadvantageous in that the motion of each cell 202 can be represented by a32-bit integer, and the frame 200 can be stored in 360 bytes (i.e.,90×32-bits). In some embodiments, this format for the motion vector 242may permit sparse data to be vastly compressed. For example, sub-cellsthat contain no motion may not need to be saved, and motion thatactivates only a single sub-cell may only consume 32-bits or 4 bytes ofdata.

FIG. 3 illustrates a flow chart of an example of a process 300 foridentifying video clips of interest, such as a video clip including anamount of motion satisfying a motion threshold. One of ordinary skillwill understood that, for any processes discussed herein, there can beadditional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments unless otherwise stated.

The process 300 may begin with step 302, in which a video feed iscaptured using a video review system, such as the video review system120. In particular, cameras (e.g., the cameras 102) of the video reviewsystem 120 can capture the video feed. The video feed can includemultiple image frames each of a predefined area (e.g., a predefineddimension).

At step 304, the video feed can be partitioned into a plurality ofblocks of pixels, such as cells and sub-cells, as illustrated above withrespect to FIGS. 2A-C. Video feed partitioning can be performed by thecameras 102, the computing devices 108, the computing devices of acloud-based management system accessible via the WAN 110, and the like.

At step 306, the frames can be processed to detect an amount of motionsatisfying a specified threshold. Each motion can be associated with atleast one block of pixels (e.g., a sub-cell). As discussed above,determinations of motion can be based on a calculation of a sum ofabsolute differences (SAD) between individual sub-cells (also “framesubtraction”). In other embodiments, different types of backgroundsubtraction calculations can be implemented; however, one of ordinaryskill will appreciate that other motion detection methods may also beused, without departing from the scope of the technology. For example,other embodiments may detect motion using temporal differencing, framedifferencing, optical flow, or a combination of these methods or acombination of background subtraction and one of these methods, amongothers.

Temporal differencing can involve calculating the difference (at a pixellevel) between successive frames to detect a moving object. Temporaldifferencing may be able to quickly adapt to highly dynamic scenechanges but may be less successful when the object stops moving and whenthe object's color texture is similar to the scene (camouflage). Also,false object detection may occur when scene objects tend to move (e.g.leaves of a tree when the air is blowing).

A simple approach of temporal differencing is frame differencing, inwhich temporal information may be indicative of moving objects in ascene. In frame differencing, the presence of mobility can beestablished by calculating the difference (at the pixel level) of twosuccessive frames.

Optical flow is the pattern of objects' motion in a scene caused by therelative motion between an observer and the scene. Optical flow can usepartial derivatives with respect to spatial and temporal coordinates tocalculate the motion between two frames. Optical flow may be moreaccurate than other approaches but can be unsuitable in situations thatmay require minimal computational time and/or low tolerance for noise.

At step 308, motion metadata can be generated based on the one or moremotions detected at step 306. As discussed above, motion metadata may begenerated as a motion vector, such as the motion vector 242, to describevarious attributes associated with the motion. Such attributes caninclude indications of time, cell location, sub-cell location,intensity, and so forth.

Next, at step 310, the motion metadata can be associated with one ormore frames and/or one or more specific blocks of pixels within arespectively associated frame. As discussed in further detail below, theassociation of motion metadata with image frames can facilitate theconvenient searching of video metadata within a region of the framespecified after the video feed has been recorded.

The process 300 may proceed to step 312 in which a motion search querymay be received, such as from a user or system administrator. The motionsearch query can be received by a search module (e.g., the search module126) that is configured to access a data store for motion metadata, suchas the data store 138 or a remote data store accessible via the WAN 110.

Although the format of the motion search query can depend on the desiredimplementation, in some embodiments, the motion search query can containinformation that defines at least a portion of the predefined pixel areafor which the desired search is to be performed. For example, the motionsearch query can take the form of a bounded area defining at least aportion of the image frame across which all of the video content is tobe searched.

In some embodiments, the motion search query can contain indications ofboundaries within the video feed. For example, the motion search querymay specify an area of the frame, in conjunction with start and stoptimes (corresponding with different frames or frame numbers in thefeed), for which the search is to be performed. Other search parameterscan include, searches based on event length and/or intensity (e.g., tofilter out sudden increases/decreases in ambient lighting). One ofordinary skill in the art will understand that other user-specifiedcriteria may alternatively or additionally be included in the motionsearch query without departing from the scope of the present technology.

The process 300 may conclude at step 314 in which the motion metadatacan be searched to identify one or more frames that include motion. Insome embodiments, the motion search query may return more specificinformation such as identifications of a specific block of pixels thatinclude motion events of interest.

Further to the example provided above with respect to FIGS. 2A-2C, if auser specifies an area of a frame that includes a specific sub-cell, thesearch could be performed across all sub-cells corresponding to theuser-specified area, across all frames. In this example, because motionhas been identified in those blocks of pixels, the associated framesand/or blocks may be returned to the user for further review.

FIGS. 4A-4E illustrate examples of graphical user interfaces for a videoreview system, which can be examples of implementations of themanagement dashboard 130 of the video review system 120. Although FIGS.4A-4E show the graphical user interfaces as pages of a web-based clientapplication displayed in a browser executing on a large form-factorgeneral purpose computing device (e.g., server, workstation, desktop,laptop, etc.), the principles disclosed in the present disclosure arewidely applicable to other types of client applications (e.g.,standalone desktop applications, mobile applications or “apps,” etc.)and client computing devices of other form factors, including tablets,smart phones, wearable devices, or other electronic devices capable ofconnecting to a local server of the video review system and/or a remoteserver of the video review system accessible via the WAN 110 andincluding input/output components to enable a user to interact with thevideo review system. One of ordinary skill will also understand that thegraphical user interfaces of FIGS. 4A-4E are but one example of a userinterface for the video review system. Other embodiments may include afewer number or a greater number of elements.

FIG. 4A illustrates an example of a graphical user interface 400A of alanding page for the video review system. In some embodiments, thelanding page may be integrated with the user interface of a networkmanagement system, such as the Cisco Meraki® dashboard. This can enablethe video review system to take advantage of the features of the Meraki®dashboard, such as centralized management of networks and devices,zero-touch deployment, automatic software updates and patches, and soforth. This can also eliminate the need for additional single-purposehardware and software, such as dedicated video review system servers,Network Video Recorders (NVRs), and the like.

The graphical user interface 400A can include video data of a scene 402,a source indicator user interface element 404, media control userinterface elements 406, a timeline user interface element 408, and asearch user interface element 410. The scene 402 may be a video feedcaptured by a camera (e.g., the camera 102) of the video review system.The source indicator user interface element 404 can indicate whether thevideo data currently being presented is stored locally or remotely.

The media control user interface elements 406 can comprise buttonsenabling a user to control the presentation of video. The top userinterface elements of the media control user interface elements 406 acan enable user to select a particular video for review, such as by acalendar pop-up menu to select a video associated with a particular dateand/or time, a drop-down menu presenting a list of videos, and so forth.At the bottom and from left to right, the media control user interfaceelements 406 can comprise buttons that, upon selection, enable a user torewind a currently presented video by a specified time (e.g., tenseconds), skip to a previous video, pause the video, skip to the nextvideo, and fast forward the video by the same or a different specifiedtime (e.g., 10 seconds, 30 seconds, etc.).

The timeline user interface element 408 can provide an indication of howmuch of a currently presented video has elapsed. The timeline userinterface element 408 can also enable a user to skip to a specificsection of the currently presented video by selecting (e.g., clicking,tapping, etc.) a specific time of the timeline user interface element408. In some embodiments, the timeline user interface element 408 canalso include graphical elements denoting sections of the currentlypresented video that contain interesting events, such as sectionscontaining motion, persons or other objects of interests, anomalies, andso forth.

The search user interface element 410 can comprise a button that, uponselection, navigates a user to a graphical user interface for searchinginteresting events included in the video, such as shown in FIG. 4B. Inparticular, FIG. 4B illustrates an example of a graphical user interface400B of a search module (e.g., the search module 126) for querying adata store (e.g., the data store 138 or a remote data store, such as aremote data store accessible via the WAN 110) to identify video clipscontaining motion. As discussed above, the graphical user interface 400Bcan enable a user to select a particular portion of the scene 402 tosearch for motion, such as by the user manipulating a virtual orphysical pointer (e.g., mouse cursor, finger, stylus, etc.) to select anarea of interest (e.g., selected area 412) at which the search modulecan focus its search.

FIG. 4C illustrates an example of a graphical user interface 400Crepresenting the search results from a video search query of interestingevents (e.g., a motion search query), such as video clips containing anamount of motion satisfying a specified threshold. The graphical userinterface 400C can include navigation control user interface elements414, search filter user interface elements 416, and motion recap images418A-E (collectively, 418). The navigation control user interfaceelements 414 enable a user to navigate back to a previous set of motionrecap images 418 or advance to the next set of motion recap images 418.

The search filter user interface elements 416 can enable the user tofilter the search results of the video search query by a specified starttime and date, end time and date, an amount of motion sensitivity (e.g.,motion intensity), video clip length, video clips including persons, andvideo clips with superimposed motion cells and/or sub-cells (e.g.,blocks of pixels). This can allow the user to review the most relevantor interesting contents of a set of video clips at once andsubstantially increases the efficiency of video review.

The motion recap images 418 can represent the search results of thevideo search query, the six motion recap images 418 summarizing thevideo clips matching the search criteria specified by a user (e.g.,within a specified time and date range, including an amount of motionabove a specified threshold, having a minimum length, including persons,including motion blocks, etc.). The motion recap images can be generatedby a motion recap image module (e.g., the motion recap image module 128)from the image data and metadata (e.g., motion, detected persons,distance (or size) of moving objects, etc.) associated with the videoclips matching the search criteria. Although the motion recap images 418are arranged in a 2×3 grid in this example, one of ordinary skill in theart will appreciate that any number of motion recap images and anyarrangement may be used in other embodiments.

In this example, each motion recap image 418 can be a still image of ascene with multiple instances of a moving object superimposed or stackedonto a single frame. The first or top-most instance of the moving objectcan be extracted from a frame of a video clip when the moving object isclosest to the camera (or the frame including the largest instance ofthe moving object). Alternatively or in addition, the first or top-mostinstance of the moving object may be extracted based on the selectedarea 412, such as from the frame when the moving object is closest tothe camera relative to the selected area 412, the frame including thelargest instance of the moving object within the selected area 412, theframe including the most complete representation of the moving object(e.g., the entire face of a person, the entire body of the person, etc.)within the selected area 412, and so forth. Alternatively or inaddition, the first or top-most instance of the moving object may beextracted based on other conditions, such as from the frame when themoving object has the least amount of blur; the frame representing themean, median, or mode in terms of the distance of the moving object fromthe camera, the size of the moving object from the camera, intensity ofmotion, dwell time (e.g., the length of time the moving object stayed atthe same exact position), and so forth; and numerous other possibilitiesas would be known by one of ordinary skill in the art and/or discussedelsewhere in the present disclosure.

In some embodiments, the other instances of the moving object may beextracted from the frames of the video clip using a non-fixed samplinginterval. For example, the motion recap image module 128 may extract themoving object from a frame based on a time threshold and an amount ofoverlap between instances of the moving object. In some embodiments, themotion recap image module may determine a bounding box of the movingobject from frame to frame and select the next instance of the movingobject to sample if the bounding box of the next instance overlaps withthe previously sampled instance by less than a threshold amount. Thethreshold amount can be an absolute value (e.g., 0 pixels), a percentage(e.g., less than 20% of the object sampled in the previous sampledframe, etc.).

In other embodiments, the motion recap image module 128 may utilize afixed sampling interval, such as per fixed unit of time (e.g., every 5seconds or every 120^(th) frame for a video recorded at 24 frames persecond (fps)), per fixed numbers of frames (e.g., 10 total frames evenlydivided across the length of the video clip), and so forth.

In still other embodiments, each motion recap image 418 may comprise asequence of still images, such as an animated Graphics InterchangeFormat (GIF) file. However, these embodiments may be less preferablethan still motion recap images because it may be distracting and moredifficult for a user to simultaneously review multiple animated images.

FIG. 4D illustrates an example of a graphical user interface 400D, whichcan be a full screen rendering of the motion recap image 418A. Themotion recap image 418A can include multiple instances of the front of amoving object 420A, 420B, . . . , and 420N (e.g., a mailman)interspersed with multiple instances of the back of the moving object422A, 422B, . . . , and 422N superposed or stacked on a single stillimage. The video review system can generate this arrangement with aparticular frame selection or sampling strategy and a particularsuperimposition or stacking strategy as discussed in further detail withrespect to FIG. 5 and elsewhere herein.

With a quick glance at the motion recap image 418A, a user canimmediately understand the motion occurring in the video clipcorresponding to the motion recap image 418A. This is an advantage overconventional video systems that only generate a thumbnail of a videoclip (typically the first frame of the clip). For example, if the camerawas positioned in front of a busy street, there may be dozens of videoclips of pedestrians or cars traveling across the front of the user'shome requiring playback of at least a portion of each video clip to seeif the video clip includes motion of an object moving towards the frontdoor.

FIG. 4E illustrates an example of a graphical user interface 400E, whichcan be a full screen rendering of the motion recap image 418Asuperimposed with cells and sub-cells 430. The graphical user interface400E can include a “Show motion blocks” user interface element 416A(e.g., a radio button or check box) for superimposing the cells andsub-cells and the “Motion sensitivity” user interface element 416B foradjusting the size of the cells to search for motion. For instance, thecells in this example are 6×4 grids of sub-cells but moving the sliderof the “Motion sensitivity” user interface element 416B can adjust thesize of the cells to different numbers of sub-cells. Other embodimentsmay use different numbers, shapes, and pixel sizes can be used for theframes, cells, and sub-cells, and some embodiments may enable users toconfigure these dimensions according to their own preferences based onan API, a command line interface, a settings file, or other userinterface.

The graphical user interface 400E also includes the timeline userinterface element 408. In some embodiments, the timeline user interfaceelement 408 can denote which frames of the video correspond to theinstances of the moving object that have been sampled to generate amotion recap image. In some embodiments, each of the instances of themoving object (e.g., the mailman) may be selectable, and upon selectionof an instance, the video review system can begin playback of the videoclip at the frame corresponding to the selected instance.

FIG. 5 illustrates a flow chart of an example of a process 500 forgenerating a motion recap image. The process 500 may be initiated aftera video review system, such as the video review system 120, receives amotion search query for video data including an amount of motionsatisfying a motion threshold, identifies at least one video clipresponsive to the motion search query, and prepares to generate andpresent, in response to the motion search query, the motion recap imageto summarize the video clip for more efficient video review.

The process 500 may begin with a step 502 in which a video review system(e.g., the video review system 120) can identify background image dataof a video clip including an amount of motion satisfying a motionthreshold. An example approach for identifying the video clip isdiscussed with respect to FIG. 3 and elsewhere throughout the presentdisclosure. The background data may be identified using backgroundsubtraction, temporal differencing, frame differencing, and/or opticalflow. In some embodiments, the video review system may utilize theMixture of Gaussians (MOG) background/foreground segmentation algorithm.MOG was introduced in the paper, “An improved adaptive backgroundmixture model for real-time tracking with shadow detection,” by P.Kadew, Tra KuPong, and R. Bowden in 2001, which is incorporated hereinby reference. MOG can model each background pixel as a mixture of KGaussian distributions (e.g., for K=3 to 5). The weights of the mixturecan represent the time proportions that those colors stay in the scene.The probable background colors may be the ones which stay longer andmore static. MOG can support certain optional parameters like length ofhistory, number of Gaussian mixtures, threshold, and so forth.

In other embodiments, other background models may be utilized toidentify the background data of the video clip, such as basic models(e.g., average, median, or histogram-based), statistical models (e.g.,other Gaussian models, other mixture models, hybrid models,nonparametric models, multi-kernel models, Support Vector Machines(SVMs), subspace learning models, etc.), cluster models (e.g., K-means,codebook, basic sequential clustering, etc.), neural network models,estimation models (e.g., Wiener, Kalman, Chebychev, etc.), fuzzy models,domain transform models (e.g., Fast Fourier Transform (FFT), DiscreteCosine Transform (DCT), wavelet transform, etc.), and so forth. In stillother embodiments, other detection and tracking algorithms may be used,temporal differencing, temporal differencing, and optical flow, amongothers. One of ordinary skill will understand how to integrate theseother algorithms with other aspects of the present technology withoutdeparting from the scope of the present disclosure.

At step 504, the video review system can generate foreground mask datafrom the video clip. This can involve applying a series of image filtersor image transformations to the video clip. For example, the series ofimage filters or image transformation can include identifying rawforeground image data from the video clip, dilating the raw foregroundimage data, eroding the dilated foreground image data, performing asecond dilation on the eroded foreground image data, performing blobsegmentation on the second dilated foreground image data, removing smallblobs from the blog segmented foreground image data, applying a Gaussianblur to the blob-segmented foreground image data with the small blobsremoved, and generating the final foreground mask data from the blurredforeground image data. The foreground mask data can segment foregroundimage data, representing a moving object in the video clip, from thebackground data.

Identifying raw foreground data can include extracting a contiguous setof pixels or “blob” that differs from the pixels of the background by acertain threshold.

Dilation can widen and enhance dark areas of a selected portion of imagedata (e.g., raw foreground data, eroded foreground data, etc.). Forexample, a dilation filter can bring the pixel value (e.g., luminosity)of the selected pixels into line with the lowest value (e.g., thedarkest value) of neighboring pixels (e.g., the 8 neighboring pixels ina 3×3 grid). Thus, a dark pixel can be added around dark areas and anisolated dark pixel against a brighter background may be enlarged (e.g.,1 dark pixel will be enlarged into a 3×3 dark pixel).

Erosion can widen and enhance bright areas of a selected portion of animage (e.g., dilated foreground data). For example, an erosion filtercan bring the pixel value (e.g., luminosity) of selected pixels intoline with the upper value (e.g., the brightest value) of neighboringpixels. Thus, a bright pixel can be added round bright areas and anisolated dark pixel against a brighter background may be “deleted.”

Blobs are regions of image data in which some properties (e.g.,brightness, color, etc.) are constant or approximately constant. Thepixels in a blob can be the same or similar according to one or morespecified metrics. A blob segmentation filter can identify blobs from aselected portion of an image (e.g., second dilated foreground data). Asmall blob filter can remove blobs of a size below a size threshold froma selected portion of image data (e.g., blog-segmented foreground data).

A Gaussian blur can “soften” the edges of a selected portion of imagedata (e.g., blog-segmented foreground data with small blobs removed).Softening to involve altering the alpha or transparency of the edges ofthe foreground blobs or blending background and foreground image data(instead of substituting a background pixel with a foreground pixelaltogether). For example, applying a Gaussian blur can average the pixelvalues of selected pixels to the average of all pixel values within aspecified radius.

At step 506, the video review system can select or sample a set ofinstances of the foreground image data from the video clip. In someembodiments, the video review system may utilize non-fixed sampling forselecting the instances. The sampling can be based on a time thresholdand an amount of overlap between instances of the moving object. In someembodiments, the video review system may calculate a bounding box of themoving object across the video clip. For example, given a first selectedor sampled frame (i.e., a previous selected or sampled frame) with afirst bounding box (i.e., a previous bounding box) and a first timestamp(i.e., a previous timestamp), the video review system may select orsample the next frame (i.e., a current frame) when the differencebetween the timestamp of the next frame (i.e., a current timestamp) andthe first timestamp (i.e., the previous timestamp) is within the timethreshold and when the amount of overlap between the bounding boxencompassing the moving object in the next frame (i.e., a currentbounding box) and the first bounding box (i.e., the previous boundingbox) is within the overlap threshold. The overlap threshold may be oneor more absolute values (e.g., less than 100 pixels×100 pixels) or arelative value (e.g., less than 10% overlap). In some embodiments, thetime threshold and overlay threshold (and generally any thresholddiscussed herein) may be configurable via an API, a command lineinterface, a settings file, or other user interface.

The process 500 may conclude at step 508 in which the video reviewsystem can generate a motion recap image by stacking the set ofinstances of the foreground data and the background data. In someembodiments, the video review system may order the stack based on anestimated distance of the moving object from the camera or the size ofthe moving object in the image or video frame, the distance of themoving object or the size of the object from the selected area 412, thedistance of the moving object or the size of the object from the centerof the image or video frame, and so forth. For example, FIG. 4D showshow a distance-based stacking approach (in combination with temporal andoverlap thresholding) can be used to interleave the front-facinginstances 420 and the back-facing instances 422 of the mailman.

FIG. 6A and FIG. 6B illustrate systems in accordance with variousembodiments. The more appropriate system will be apparent to those ofordinary skill in the art when practicing the various embodiments.Persons of ordinary skill in the art will also readily appreciate thatother systems are possible.

FIG. 6A illustrates an example of a bus computing system 600 wherein thecomponents of the system are in electrical communication with each otherusing a bus 605. The computing system 600 can include a processing unit(CPU or processor) 610 and a system bus 605 that may couple varioussystem components including the system memory 615, such as read onlymemory (ROM) 620 and random access memory (RAM) 625, to the processor610. The computing system 600 can include a cache 612 of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 610. The computing system 600 can copy data fromthe memory 615, ROM 620, RAM 625, and/or storage device 630 to the cache612 for quick access by the processor 610. In this way, the cache 612can provide a performance boost that avoids processor delays whilewaiting for data. These and other modules can control the processor 610to perform various actions. Other system memory 615 may be available foruse as well. The memory 615 can include multiple different types ofmemory with different performance characteristics. The processor 610 caninclude any general purpose processor and a hardware module or softwaremodule, such as module 1 632, module 2 634, and module 3 636 stored inthe storage device 630, configured to control the processor 610 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 610 mayessentially be a completely self-contained computing system, containingmultiple cores or processors, a bus, memory controller, cache, etc. Amulti-core processor may be symmetric or asymmetric.

To enable user interaction with the computing system 600, an inputdevice 645 can represent any number of input mechanisms, such as amicrophone for speech, a touch-protected screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 635 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing system 600. The communications interface640 can govern and manage the user input and system output. There may beno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

The storage device 630 can be a non-volatile memory and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memory, read only memory, and hybrids thereof.

As discussed above, the storage device 630 can include the softwaremodules 632, 634, 636 for controlling the processor 610. Other hardwareor software modules are contemplated. The storage device 630 can beconnected to the system bus 605. In some embodiments, a hardware modulethat performs a particular function can include a software componentstored in a computer-readable medium in connection with the necessaryhardware components, such as the processor 610, bus 605, output device635, and so forth, to carry out the function.

FIG. 6B illustrates an example architecture for a chipset computingsystem 650 that can be used in accordance with an embodiment. Thecomputing system 650 can include a processor 655, representative of anynumber of physically and/or logically distinct resources capable ofexecuting software, firmware, and hardware configured to performidentified computations. The processor 655 can communicate with achipset 660 that can control input to and output from the processor 655.In this example, the chipset 660 can output information to an outputdevice 665, such as a display, and can read and write information tostorage device 670, which can include magnetic media, solid state media,and other suitable storage media. The chipset 660 can also read datafrom and write data to RAM 675. A bridge 680 for interfacing with avariety of user interface components 685 can be provided for interfacingwith the chipset 660. The user interface components 685 can include akeyboard, a microphone, touch detection and processing circuitry, apointing device, such as a mouse, and so on. Inputs to the computingsystem 650 can come from any of a variety of sources, machine generatedand/or human generated.

The chipset 660 can also interface with one or more communicationinterfaces 690 that can have different physical interfaces. Thecommunication interfaces 690 can include interfaces for wired andwireless Local Area Networks (LANs), for broadband wireless networks, aswell as personal area networks. Some applications of the methods forgenerating, displaying, and using the technology disclosed herein caninclude receiving ordered datasets over the physical interface or begenerated by the machine itself by the processor 655 analyzing datastored in the storage device 670 or the RAM 675. Further, the computingsystem 650 can receive inputs from a user via the user interfacecomponents 685 and execute appropriate functions, such as browsingfunctions by interpreting these inputs using the processor 655.

It will be appreciated that computing systems 600 and 650 can have morethan one processor 610 and 655, respectively, or be part of a group orcluster of computing devices networked together to provide greaterprocessing capability.

For clarity of explanation, in some instances the various embodimentsmay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, Universal Serial Bus (USB) devices provided withnon-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Some examples of such form factors include general purposecomputing devices such as servers, rack mount devices, desktopcomputers, laptop computers, and so on, or general purpose mobilecomputing devices, such as tablet computers, smart phones, personaldigital assistants, wearable devices, and so on. Functionality describedherein also can be embodied in peripherals or add-in cards. Suchfunctionality can also be implemented on a circuit board among differentchips or different processes executing in a single device, by way offurther example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The invention claimed is:
 1. A method comprising: detecting one or moremotions in a video feed; generating motion vectors describing each ofthe one or more motions; associating the motion vectors with one or moreblocks of pixels of one or more frames of the video feed; receiving aquery; identifying, from the motion vectors, at least a portion of thevideo feed that includes a motion of the one or more motionscorresponding to the query; and generating a motion recap image of themotion based at least in part on the motion vectors.
 2. The method ofclaim 1, wherein the video feed includes at least a plurality of framesof a predefined pixel area.
 3. The method of claim 2, furthercomprising: partitioning the plurality of frames into a plurality ofblocks of pixels.
 4. The method of claim 3, wherein each motion of theone or more motions is associated with at least one block of pixels ofthe plurality of blocks of pixels.
 5. The method of claim 3, whereineach block of pixels of the plurality of blocks of pixels includes asubset of the predefined pixel area of a corresponding frame of theplurality of frames.
 6. The method of claim 3, wherein the motionvectors are associated with the one or more blocks of pixels of theplurality of blocks of pixels that correspond with at least one of theone or more motions that occurred across one or more frames of theplurality of frames.
 7. The method of claim 2, wherein the portion ofthe video feed includes at least one of the plurality of frames.
 8. Themethod of claim 2, wherein the query includes at least a portion of thepredefined pixel area.
 9. The method of claim 8, the identifying furthercomprising: searching at least one of the motion vectors at thepredefined pixel area for one or more frames of the video feed and blockpairs that include a motion of the one or more motions corresponding tothe query.
 10. The method of claim 1, wherein generating the motionrecap image includes: identifying background image data of the portionof the video feed; generating foreground mask data by segmentingforeground image data from the background image data; and selecting aset of instances of the one or more motions from the foreground imagedata.
 11. The method of claim 10, wherein the foreground image datarepresents the one or more motions.
 12. The method of claim 10, whereinthe motion recap image is generated by superimposing the set ofinstances of the one or more motions onto the background image data. 13.A system comprising: at least one processor; and a memory storinginstructions, which when executed by the at least one processor, causesthe at least one processor to: detect one or more motions in a videofeed; generate motion vectors describing each of the one or moremotions; associate the motion vectors with one or more blocks of pixelsof one or more frames of the video feed; receive a query; identify, fromthe motion vectors, at least a portion of the video feed that includes amotion of the one or more motions corresponding to the query; andgenerate a motion recap image of the motion based at least in part onthe motion vectors.
 14. The system of claim 13, wherein the video feedincludes at least a plurality of frames of a predefined pixel area. 15.The system of claim 14, wherein the motion vectors are associated withthe one or more blocks of pixels that correspond with at least one ofthe one or more motions that occurred across one or more frames of thevideo feed.
 16. The system of claim 15, further comprising instructions,which when executed by the at least one processor, causes the at leastone processor to: search at least one of the motion vectors at thepredefined pixel area for one or more frames of the video feed and blockpairs that include a motion of the one or more motions corresponding tothe query.
 17. The system of claim 13, further comprising instructions,which when executed by the at least one processor, causes the at leastone processor to generate the motion recap image by: identify backgroundimage data of the portion of the video feed; generate foreground maskdata by segmenting foreground image data from the background image data;and select a set of instances of the one or more motions from theforeground image data.
 18. At least one non-transitory computer readablemedium storing instructions, which when executed by at least oneprocessor, causes the at least one processor to: detect one or moremotions in a video feed; generate motion vectors describing each of theone or more motions; associate the motion vectors with one or moreblocks of pixels of one or more frames of the video feed; receive aquery; identify, from the motion vectors, at least a portion of thevideo feed that includes a motion of the one or more motionscorresponding to the query; and generate a motion recap image of themotion based at least in part on the metadata.
 19. The at least onenon-transitory computer readable medium of claim 18, wherein the motionvectors are associated with the one or more blocks of pixels thatcorrespond with at least one of the one or more motions that occurredacross one or more frames of the video feed.
 20. The at least onenon-transitory computer readable medium of claim 18, further comprisinginstructions, which when executed by the at least one processor, causesthe at least one processor to generate the motion recap image by:identify background image data of the portion of the video feed;generate foreground mask data by segmenting foreground image data fromthe background image data; and select a set of instances of the one ormore motions from the foreground image data.